Model prediction of soil drainage classes based on digital elevation model parameters and soil attributes from coarse resolution soil maps

Zhao, Zhengyong; Chow, T. L.; Yang, Qi; Rees, H. W.; Benoy, Glenn; Xing, Zisheng; Meng, Fan‐Rui

doi:10.4141/cjss08012

Cited by 30 publications

(32 citation statements)

References 28 publications

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“…The lack of an independent dataset to validate Rs models is typical of similar studies [ Bond‐Lamberty and Thomson , ; Chen et al ., , ; Hashimoto et al ., ; Li et al ., ]. For this study, according to methods that have been accepted in the ANN modeling community [ Zhao et al ., ; Wen et al ., ], one dataset (including one of candidate inputs and corresponding Rs field records) was first divided into a calibration dataset (80% of the total) and a validation dataset (20% of the total). The calibration dataset was used to build the ANN model, and the validation dataset was used to examine the performance of the ANN model.…”

Section: Methodsmentioning

confidence: 99%

“…The lack of an independent dataset to validate Rs models is typical of similar studies [Bond-Lamberty and Thomson, 2010;Chen et al, 2010Chen et al, , 2013Hashimoto et al, 2015;Li et al, 2017]. For this study, according to methods that have been accepted in the ANN modeling community [Zhao et al, 2008;Wen et al, 2014], one dataset (including one ANN, artificial neural network; MAT, mean annual temperature; MAP, mean annual precipitation; SOC, soil organic carbon; LAI, leaf area index; NPP, net primary production. a Correlation coefficients between the field-measured annual Rs and predicted annual Rs using an linear model that used a single predictor as input.…”

Section: Artificial Neural Network Modelmentioning

confidence: 99%

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Model prediction of biome‐specific global soil respiration from 1960 to 2012

et al. 2017

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Biome-specific soil respiration (Rs) has important yet different roles in both the carbon cycle and climate change from regional to global scales. To date, no comparable studies related to global biome-specific Rs have been conducted applying comprehensive global Rs databases. The goal of this study was to develop artificial neural network (ANN) models capable of spatially estimating global Rs and to evaluate the effects of interannual climate variations on 10 major biomes. We used 1976 annual Rs field records extracted from global Rs literature to train and test the ANN models. We determined that the best ANN model for predicting biome-specific global annual Rs was the one that applied mean annual temperature (MAT), mean annual precipitation (MAP), and biome type as inputs (r 2 = 0.60). The ANN models reported an average global Rs of 93.3 ± 6.1 Pg C yr −1 from 1960 to 2012 and an increasing trend in average global annual Rs of 0.04 Pg C yr −1 . Estimated annual Rs increased with increases in MAT and MAP in cropland, boreal forest, grassland, shrubland, and wetland biomes. Additionally, estimated annual Rs decreased with increases in MAT and increased with increases in MAP in desert and tundra biomes, and only significantly decreased with increases in MAT (r 2 = 0.87) in the savannah biome. The developed biome-specific global Rs database for global land and soil carbon models will aid in understanding the mechanisms underlying variations in soil carbon dynamics and in quantifying uncertainty in the global soil carbon cycle.

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Section: Methodsmentioning

confidence: 99%

Section: Artificial Neural Network Modelmentioning

confidence: 99%

Model prediction of biome‐specific global soil respiration from 1960 to 2012

et al. 2017

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“…This is based on the possibility that the original land cover types (forest) before settlement had impacts on soil formation for a long time compared with current land uses, which may have had relatively less time to affect soil formation. The SDR map was generated with the Spatial Analyst Extension of ArcGIS (ESRI 1999Á2005;Zhao et al 2008). …”

Section: Variables Derived From Digital Elevation Modelmentioning

confidence: 99%

“…A number of researchers have shown that the ANN model can be used to establish relationships through linear or non-linear functions (Levine et al 1996;Levine and Kimes 1997;McBratney et al 2000;Licznar and Nearing 2003;Zhao et al 2008). In this study, we developed an ANN model to predict SOC content based on soil attributes extracted from widely available coarse resolution maps (1:1 000 000 scale) and digital elevation model (DEM) derived parameters (1:35 000 scale).…”

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confidence: 99%

Using artificial neural network models to produce soil organic carbon content distribution maps across landscapes

Zhao¹,

Yang²,

Benoy³

et al. 2010

Can. J. Soil. Sci.

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network models to produce soil organic carbon content distribution maps across landscapes. Can. J. Soil Sci. 90: 75Á87. Soil organic carbon (SOC) content is an important soil quality indicator that plays an important role in regulating physical, chemical and biological properties of soil. Field assessment of SOC is time consuming and expensive. It is difficult to obtain highresolution SOC distribution maps that are needed for landscape analysis of large areas. An artificial neural network (ANN) model was developed to predict SOC based on parameters derived from digital elevation model (DEM) together with soil properties extracted from widely available coarse resolution soil maps (1:1 000 000 scale). Field estimated SOC content data extracted from high-resolution soil maps (1:10 000 scale) in Black Brook Watershed in northwestern New Brunswick, Canada, were used to calibrate and validate the model. We found that vertical slope position (VSP) was the most important variable that determines distributions of SOC across the landscape. Other variables such as slope steepness, and potential solar radiation (PSR) also had significant influence on SOC distributions. The prediction of the selected two-input-node SOC model (VSP and coarse resolution soil map recorded SOC as inputs) had a correlation coefficient of 0.92 with measured values, and model predicted SOC values had 47.9% of the total points within 90.5% of the measured values and 70.6% within 91% of the measured values. The prediction of the selected four-input-node model (VSP, slope steepness, PSR and coarse resolution SOC values as inputs) had a correlation coefficient of 0.98 with measured values and model predicted SOC values had 75% of the total points within 90.5% of the measured values and 87% within 91% of the measured values. The prediction of the five-input-nodes model with soil drainage as additional input had a correlation coefficient of 0.99 with measured values, and model predicted SOC values had 87% of the total points within 90.5% of the measured values and 98% of the total points within 91% of the measured values. The calibrated SOC prediction model was used to produce a high-resolution SOC map for the Black Brook Watershed and the resulting SOC distribution map is considered to be realistic. Results indicated that DEM-derived hydrological parameters together with widely available coarse resolution soil map data could be used to produce high-resolution SOC maps with the ANN method. For personal use only.

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“…The climate of the region is considered to be moderately cool boreal with approximately 120 frost-free days [2]. The average temperature is 3.7 • C and annual precipitation is 1037.4 mm [21,25,26]. About one-third of the precipitation is in the form of snow.…”

Section: Study Areamentioning

confidence: 99%

Hydrological Evaluation of Flow Diversion Terraces Using Downhill-Slope Calculation Method for High Resolution and Accuracy DEMs

Liang

et al. 2018

Sustainability

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Slope is an important topographical parameter in hydrological models. Dramatic changes in slope are produced due to micro variations of topography in flow diversion terrace (FDT) systems. Assessing the effectiveness of FDT on soil conservation yields different results using different slope calculation methods with different resolution and accuracy digital elevation models (DEMs). In this study, the ArcGIS built-in average-neighborhood-slope (ANS) method and a downhill-slope (DHS) method were used to calculate slopes for a small agricultural watershed in New Brunswick, Canada. Six DEMs were used, i.e., 1, 5 and 10 m resolution DEMs generated with conventional photogrammetric techniques, and 1, 5, and 10 m resolution DEMs derived from light detection and ranging (LiDAR) data. Calculated slopes were summarized for the whole watershed, a patch of FDT systems, and grassed flow channels in front of embankments in the patch. Results indicated that the DHS method produced smaller slopes than the ANS method with LiDAR-1m DEM along grasses flow channels. Furthermore, the mean L-factor derived from the DHS method was smaller than that derived from the ANS in terraces and grassed flow channels for the LiDAR-1m DEM. Finally, soil loss estimated from the DHS method using RUSLE was more consistent with measurement for the watershed than that from the ANS method when the LiDAR-1m DEM was used. The DHS method can provide accurate slopes along grassed flow channels and can be used to assess FDT systems using USLE-based models with high resolution and accuracy DEMs.

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Model prediction of soil drainage classes based on digital elevation model parameters and soil attributes from coarse resolution soil maps

Cited by 30 publications

References 28 publications

Model prediction of biome‐specific global soil respiration from 1960 to 2012

Model prediction of biome‐specific global soil respiration from 1960 to 2012

Using artificial neural network models to produce soil organic carbon content distribution maps across landscapes

Hydrological Evaluation of Flow Diversion Terraces Using Downhill-Slope Calculation Method for High Resolution and Accuracy DEMs

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